diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 9a2d1ae..377fc66 100755
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,10 @@
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "from scipy import stats\n",
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.cluster import DBSCAN"
    ]
   },
   {
@@ -38,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,11 +70,117 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicassen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12669</td>\n",
+       "      <td>9656</td>\n",
+       "      <td>7561</td>\n",
+       "      <td>214</td>\n",
+       "      <td>2674</td>\n",
+       "      <td>1338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7057</td>\n",
+       "      <td>9810</td>\n",
+       "      <td>9568</td>\n",
+       "      <td>1762</td>\n",
+       "      <td>3293</td>\n",
+       "      <td>1776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6353</td>\n",
+       "      <td>8808</td>\n",
+       "      <td>7684</td>\n",
+       "      <td>2405</td>\n",
+       "      <td>3516</td>\n",
+       "      <td>7844</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13265</td>\n",
+       "      <td>1196</td>\n",
+       "      <td>4221</td>\n",
+       "      <td>6404</td>\n",
+       "      <td>507</td>\n",
+       "      <td>1788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22615</td>\n",
+       "      <td>5410</td>\n",
+       "      <td>7198</td>\n",
+       "      <td>3915</td>\n",
+       "      <td>1777</td>\n",
+       "      <td>5185</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
+       "0        2       3  12669  9656     7561     214              2674        1338\n",
+       "1        2       3   7057  9810     9568    1762              3293        1776\n",
+       "2        2       3   6353  8808     7684    2405              3516        7844\n",
+       "3        1       3  13265  1196     4221    6404               507        1788\n",
+       "4        2       3  22615  5410     7198    3915              1777        5185"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "customers.head()"
    ]
   },
   {
@@ -80,7 +189,461 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your observations here"
+    "# Columns appear to correspond to type of groceries customers make"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 440 entries, 0 to 439\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column            Non-Null Count  Dtype\n",
+      "---  ------            --------------  -----\n",
+      " 0   Channel           440 non-null    int64\n",
+      " 1   Region            440 non-null    int64\n",
+      " 2   Fresh             440 non-null    int64\n",
+      " 3   Milk              440 non-null    int64\n",
+      " 4   Grocery           440 non-null    int64\n",
+      " 5   Frozen            440 non-null    int64\n",
+      " 6   Detergents_Paper  440 non-null    int64\n",
+      " 7   Delicassen        440 non-null    int64\n",
+      "dtypes: int64(8)\n",
+      "memory usage: 27.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "customers.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Channel             0\n",
+       "Region              0\n",
+       "Fresh               0\n",
+       "Milk                0\n",
+       "Grocery             0\n",
+       "Frozen              0\n",
+       "Detergents_Paper    0\n",
+       "Delicassen          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customers.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicassen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>440.00</td>\n",
+       "      <td>440.00</td>\n",
+       "      <td>440.00</td>\n",
+       "      <td>440.00</td>\n",
+       "      <td>440.00</td>\n",
+       "      <td>440.00</td>\n",
+       "      <td>440.00</td>\n",
+       "      <td>440.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.32</td>\n",
+       "      <td>2.54</td>\n",
+       "      <td>12000.30</td>\n",
+       "      <td>5796.27</td>\n",
+       "      <td>7951.28</td>\n",
+       "      <td>3071.93</td>\n",
+       "      <td>2881.49</td>\n",
+       "      <td>1524.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.47</td>\n",
+       "      <td>0.77</td>\n",
+       "      <td>12647.33</td>\n",
+       "      <td>7380.38</td>\n",
+       "      <td>9503.16</td>\n",
+       "      <td>4854.67</td>\n",
+       "      <td>4767.85</td>\n",
+       "      <td>2820.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>55.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>25.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>3.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>3127.75</td>\n",
+       "      <td>1533.00</td>\n",
+       "      <td>2153.00</td>\n",
+       "      <td>742.25</td>\n",
+       "      <td>256.75</td>\n",
+       "      <td>408.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>8504.00</td>\n",
+       "      <td>3627.00</td>\n",
+       "      <td>4755.50</td>\n",
+       "      <td>1526.00</td>\n",
+       "      <td>816.50</td>\n",
+       "      <td>965.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>16933.75</td>\n",
+       "      <td>7190.25</td>\n",
+       "      <td>10655.75</td>\n",
+       "      <td>3554.25</td>\n",
+       "      <td>3922.00</td>\n",
+       "      <td>1820.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>112151.00</td>\n",
+       "      <td>73498.00</td>\n",
+       "      <td>92780.00</td>\n",
+       "      <td>60869.00</td>\n",
+       "      <td>40827.00</td>\n",
+       "      <td>47943.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Channel  Region      Fresh      Milk   Grocery    Frozen  \\\n",
+       "count   440.00  440.00     440.00    440.00    440.00    440.00   \n",
+       "mean      1.32    2.54   12000.30   5796.27   7951.28   3071.93   \n",
+       "std       0.47    0.77   12647.33   7380.38   9503.16   4854.67   \n",
+       "min       1.00    1.00       3.00     55.00      3.00     25.00   \n",
+       "25%       1.00    2.00    3127.75   1533.00   2153.00    742.25   \n",
+       "50%       1.00    3.00    8504.00   3627.00   4755.50   1526.00   \n",
+       "75%       2.00    3.00   16933.75   7190.25  10655.75   3554.25   \n",
+       "max       2.00    3.00  112151.00  73498.00  92780.00  60869.00   \n",
+       "\n",
+       "       Detergents_Paper  Delicassen  \n",
+       "count            440.00      440.00  \n",
+       "mean            2881.49     1524.87  \n",
+       "std             4767.85     2820.11  \n",
+       "min                3.00        3.00  \n",
+       "25%              256.75      408.25  \n",
+       "50%              816.50      965.50  \n",
+       "75%             3922.00     1820.25  \n",
+       "max            40827.00    47943.00  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customers.describe().round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicassen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Channel</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.062028</td>\n",
+       "      <td>-0.169172</td>\n",
+       "      <td>0.460720</td>\n",
+       "      <td>0.608792</td>\n",
+       "      <td>-0.202046</td>\n",
+       "      <td>0.636026</td>\n",
+       "      <td>0.056011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>0.062028</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.055287</td>\n",
+       "      <td>0.032288</td>\n",
+       "      <td>0.007696</td>\n",
+       "      <td>-0.021044</td>\n",
+       "      <td>-0.001483</td>\n",
+       "      <td>0.045212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fresh</th>\n",
+       "      <td>-0.169172</td>\n",
+       "      <td>0.055287</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.100510</td>\n",
+       "      <td>-0.011854</td>\n",
+       "      <td>0.345881</td>\n",
+       "      <td>-0.101953</td>\n",
+       "      <td>0.244690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Milk</th>\n",
+       "      <td>0.460720</td>\n",
+       "      <td>0.032288</td>\n",
+       "      <td>0.100510</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.728335</td>\n",
+       "      <td>0.123994</td>\n",
+       "      <td>0.661816</td>\n",
+       "      <td>0.406368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Grocery</th>\n",
+       "      <td>0.608792</td>\n",
+       "      <td>0.007696</td>\n",
+       "      <td>-0.011854</td>\n",
+       "      <td>0.728335</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.040193</td>\n",
+       "      <td>0.924641</td>\n",
+       "      <td>0.205497</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Frozen</th>\n",
+       "      <td>-0.202046</td>\n",
+       "      <td>-0.021044</td>\n",
+       "      <td>0.345881</td>\n",
+       "      <td>0.123994</td>\n",
+       "      <td>-0.040193</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.131525</td>\n",
+       "      <td>0.390947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <td>0.636026</td>\n",
+       "      <td>-0.001483</td>\n",
+       "      <td>-0.101953</td>\n",
+       "      <td>0.661816</td>\n",
+       "      <td>0.924641</td>\n",
+       "      <td>-0.131525</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.069291</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delicassen</th>\n",
+       "      <td>0.056011</td>\n",
+       "      <td>0.045212</td>\n",
+       "      <td>0.244690</td>\n",
+       "      <td>0.406368</td>\n",
+       "      <td>0.205497</td>\n",
+       "      <td>0.390947</td>\n",
+       "      <td>0.069291</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Channel    Region     Fresh      Milk   Grocery    Frozen  \\\n",
+       "Channel           1.000000  0.062028 -0.169172  0.460720  0.608792 -0.202046   \n",
+       "Region            0.062028  1.000000  0.055287  0.032288  0.007696 -0.021044   \n",
+       "Fresh            -0.169172  0.055287  1.000000  0.100510 -0.011854  0.345881   \n",
+       "Milk              0.460720  0.032288  0.100510  1.000000  0.728335  0.123994   \n",
+       "Grocery           0.608792  0.007696 -0.011854  0.728335  1.000000 -0.040193   \n",
+       "Frozen           -0.202046 -0.021044  0.345881  0.123994 -0.040193  1.000000   \n",
+       "Detergents_Paper  0.636026 -0.001483 -0.101953  0.661816  0.924641 -0.131525   \n",
+       "Delicassen        0.056011  0.045212  0.244690  0.406368  0.205497  0.390947   \n",
+       "\n",
+       "                  Detergents_Paper  Delicassen  \n",
+       "Channel                   0.636026    0.056011  \n",
+       "Region                   -0.001483    0.045212  \n",
+       "Fresh                    -0.101953    0.244690  \n",
+       "Milk                      0.661816    0.406368  \n",
+       "Grocery                   0.924641    0.205497  \n",
+       "Frozen                   -0.131525    0.390947  \n",
+       "Detergents_Paper          1.000000    0.069291  \n",
+       "Delicassen                0.069291    1.000000  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customers.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 1, 2], dtype=int64)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customers[\"Region\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 1], dtype=int64)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customers[\"Channel\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "suptitle() missing 1 required positional argument: 't'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[12], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m columns_1 \u001b[38;5;241m=\u001b[39m customers\u001b[38;5;241m.\u001b[39mselect_dtypes(include\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfloat64\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mint64\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m      2\u001b[0m columns_1\u001b[38;5;241m.\u001b[39mhist(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m8\u001b[39m), bins\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m, grid\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m----> 3\u001b[0m plt\u001b[38;5;241m.\u001b[39msuptitle(y\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.02\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m16\u001b[39m)\n\u001b[0;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n\u001b[0;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
+      "\u001b[1;31mTypeError\u001b[0m: suptitle() missing 1 required positional argument: 't'"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "columns_1 = customers.select_dtypes(include=['float64', 'int64'])\n",
+    "columns_1.hist(figsize=(12, 8), bins=20, grid=False)\n",
+    "plt.suptitle(y=1.02, fontsize=16)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
@@ -107,7 +670,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your comment here"
+    "# the dataset has no null values and currently set as intergers"
    ]
   },
   {
@@ -127,15 +690,308 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicassen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12669</td>\n",
+       "      <td>9656</td>\n",
+       "      <td>7561</td>\n",
+       "      <td>214</td>\n",
+       "      <td>2674</td>\n",
+       "      <td>1338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7057</td>\n",
+       "      <td>9810</td>\n",
+       "      <td>9568</td>\n",
+       "      <td>1762</td>\n",
+       "      <td>3293</td>\n",
+       "      <td>1776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6353</td>\n",
+       "      <td>8808</td>\n",
+       "      <td>7684</td>\n",
+       "      <td>2405</td>\n",
+       "      <td>3516</td>\n",
+       "      <td>7844</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
+       "0        2       3  12669  9656     7561     214              2674        1338\n",
+       "1        2       3   7057  9810     9568    1762              3293        1776\n",
+       "2        2       3   6353  8808     7684    2405              3516        7844"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customers.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicassen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.448652</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>0.052933</td>\n",
+       "      <td>0.523568</td>\n",
+       "      <td>-0.041115</td>\n",
+       "      <td>-0.589367</td>\n",
+       "      <td>-0.043569</td>\n",
+       "      <td>-0.066339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.448652</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>-0.391302</td>\n",
+       "      <td>0.544458</td>\n",
+       "      <td>0.170318</td>\n",
+       "      <td>-0.270136</td>\n",
+       "      <td>0.086407</td>\n",
+       "      <td>0.089151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.448652</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>-0.447029</td>\n",
+       "      <td>0.408538</td>\n",
+       "      <td>-0.028157</td>\n",
+       "      <td>-0.137536</td>\n",
+       "      <td>0.133232</td>\n",
+       "      <td>2.243293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.690297</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>0.100111</td>\n",
+       "      <td>-0.624020</td>\n",
+       "      <td>-0.392977</td>\n",
+       "      <td>0.687144</td>\n",
+       "      <td>-0.498588</td>\n",
+       "      <td>0.093411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.448652</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>0.840239</td>\n",
+       "      <td>-0.052396</td>\n",
+       "      <td>-0.079356</td>\n",
+       "      <td>0.173859</td>\n",
+       "      <td>-0.231918</td>\n",
+       "      <td>1.299347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>-0.690297</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>1.401312</td>\n",
+       "      <td>0.848446</td>\n",
+       "      <td>0.850760</td>\n",
+       "      <td>2.075222</td>\n",
+       "      <td>-0.566831</td>\n",
+       "      <td>0.241091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>-0.690297</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>2.155293</td>\n",
+       "      <td>-0.592142</td>\n",
+       "      <td>-0.757165</td>\n",
+       "      <td>0.296561</td>\n",
+       "      <td>-0.585519</td>\n",
+       "      <td>0.291501</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>1.448652</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>0.200326</td>\n",
+       "      <td>1.314671</td>\n",
+       "      <td>2.348386</td>\n",
+       "      <td>-0.543380</td>\n",
+       "      <td>2.511218</td>\n",
+       "      <td>0.121456</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>-0.690297</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>-0.135384</td>\n",
+       "      <td>-0.517536</td>\n",
+       "      <td>-0.602514</td>\n",
+       "      <td>-0.419441</td>\n",
+       "      <td>-0.569770</td>\n",
+       "      <td>0.213046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>-0.690297</td>\n",
+       "      <td>0.590668</td>\n",
+       "      <td>-0.729307</td>\n",
+       "      <td>-0.555924</td>\n",
+       "      <td>-0.573227</td>\n",
+       "      <td>-0.620094</td>\n",
+       "      <td>-0.504888</td>\n",
+       "      <td>-0.522869</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>440 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Channel    Region     Fresh      Milk   Grocery    Frozen  \\\n",
+       "0    1.448652  0.590668  0.052933  0.523568 -0.041115 -0.589367   \n",
+       "1    1.448652  0.590668 -0.391302  0.544458  0.170318 -0.270136   \n",
+       "2    1.448652  0.590668 -0.447029  0.408538 -0.028157 -0.137536   \n",
+       "3   -0.690297  0.590668  0.100111 -0.624020 -0.392977  0.687144   \n",
+       "4    1.448652  0.590668  0.840239 -0.052396 -0.079356  0.173859   \n",
+       "..        ...       ...       ...       ...       ...       ...   \n",
+       "435 -0.690297  0.590668  1.401312  0.848446  0.850760  2.075222   \n",
+       "436 -0.690297  0.590668  2.155293 -0.592142 -0.757165  0.296561   \n",
+       "437  1.448652  0.590668  0.200326  1.314671  2.348386 -0.543380   \n",
+       "438 -0.690297  0.590668 -0.135384 -0.517536 -0.602514 -0.419441   \n",
+       "439 -0.690297  0.590668 -0.729307 -0.555924 -0.573227 -0.620094   \n",
+       "\n",
+       "     Detergents_Paper  Delicassen  \n",
+       "0           -0.043569   -0.066339  \n",
+       "1            0.086407    0.089151  \n",
+       "2            0.133232    2.243293  \n",
+       "3           -0.498588    0.093411  \n",
+       "4           -0.231918    1.299347  \n",
+       "..                ...         ...  \n",
+       "435         -0.566831    0.241091  \n",
+       "436         -0.585519    0.291501  \n",
+       "437          2.511218    0.121456  \n",
+       "438         -0.569770    0.213046  \n",
+       "439         -0.504888   -0.522869  \n",
+       "\n",
+       "[440 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your import here:\n",
     "\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "\n",
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "\n",
+    "\n",
+    "columns_2  = customers.select_dtypes(include=['float64', 'int64'])\n",
+    "scaler  = StandardScaler()\n",
+    "customers_scale = scaler.fit_transform(columns_2)\n",
+    "\n",
+    "customers_scale_df = pd.DataFrame(customers_scale, columns=columns_2.columns)\n",
+    "\n",
+    "customers_scale_df\n",
+    "\n"
    ]
   },
   {
@@ -149,13 +1005,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Elsio Miranda\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Elsio Miranda\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Elsio Miranda\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Elsio Miranda\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Elsio Miranda\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Elsio Miranda\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Elsio Miranda\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Elsio Miranda\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Elsio Miranda\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Elsio Miranda\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(close=None, block=None)>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "k_value = range (1, 11)\n",
+    "inercias=[]\n",
+    "\n",
+    "\n",
+    "for k in k_value:\n",
+    "    k_means = KMeans(n_clusters=k, random_state=42)\n",
+    "    k_means.fit(customers_scale_df)\n",
+    "    inercias.append(k_means.inertia_)\n",
+    "\n",
+    "\n",
+    "plt.plot(k_value, inercias, marker = 'o')\n",
+    "plt.show"
    ]
   },
   {
@@ -167,11 +1082,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Elsio Miranda\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  \\\n",
+      "0        2       3  12669  9656     7561     214              2674   \n",
+      "1        2       3   7057  9810     9568    1762              3293   \n",
+      "2        2       3   6353  8808     7684    2405              3516   \n",
+      "3        1       3  13265  1196     4221    6404               507   \n",
+      "4        2       3  22615  5410     7198    3915              1777   \n",
+      "\n",
+      "   Delicassen  labels  \n",
+      "0        1338       0  \n",
+      "1        1776       0  \n",
+      "2        7844       0  \n",
+      "3        1788       1  \n",
+      "4        5185       0  \n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "scaler = StandardScaler()\n",
+    "customers_scale = scaler.fit_transform(columns_2)\n",
+    "customers_scale_df = pd.DataFrame(customers_scale, columns=columns_2.columns)\n",
+    "k = 4\n",
+    "\n",
+    "k_means = KMeans(n_clusters=k, random_state=42)\n",
+    "\n",
+    "labels_clusters = k_means.fit_predict(customers_scale_df)\n",
+    "customers['labels'] = labels_clusters\n",
+    "\n",
+    "print(customers.head())\n"
    ]
   },
   {
@@ -185,13 +1138,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  \\\n",
+      "0        2       3  12669  9656     7561     214              2674   \n",
+      "1        2       3   7057  9810     9568    1762              3293   \n",
+      "2        2       3   6353  8808     7684    2405              3516   \n",
+      "3        1       3  13265  1196     4221    6404               507   \n",
+      "4        2       3  22615  5410     7198    3915              1777   \n",
+      "\n",
+      "   Delicassen  labels  label_dbscan  \n",
+      "0        1338       0            -1  \n",
+      "1        1776       0            -1  \n",
+      "2        7844       0            -1  \n",
+      "3        1788       1             1  \n",
+      "4        5185       0            -1  \n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "eps_value = 0.5\n",
+    "\n",
+    "model_dbscan = DBSCAN(eps=eps_value)\n",
+    "\n",
+    "label_dbscan = model_dbscan.fit_predict(customers_scale_df)\n",
+    "\n",
+    "customers['label_dbscan'] = label_dbscan\n",
+    "\n",
+    "print(customers.head())"
    ]
   },
   {
@@ -203,11 +1184,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "label_dbscan\n",
+       "-1    255\n",
+       " 1    130\n",
+       " 5     22\n",
+       " 4      7\n",
+       " 6      6\n",
+       " 0      5\n",
+       " 3      5\n",
+       " 2      5\n",
+       " 7      5\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "count_labels_dbscan = customers['label_dbscan'].value_counts()\n",
+    "count_labels_dbscan"
    ]
   },
   {
@@ -234,11 +1238,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\n",
+    "\n",
+    "axes[0].scatter(customers['Detergents_Paper'], customers['Milk'], c=customers['labels'], cmap='viridis')\n",
+    "\n",
+    "axes[1].scatter(customers['Detergents_Paper'], customers['Milk'], c=customers['label_dbscan'], cmap='viridis')\n",
+    "axes[1].set_title('DBSCAN Clustering')\n",
+    "axes[1].set_xlabel('Detergents_Paper')\n",
+    "axes[1].set_ylabel('Milk')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -250,11 +1279,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "fig, axs = plt.subplots(1, 2,  figsize=(12, 5))\n",
+    "\n",
+    "axs[0].scatter(customers['Grocery'], customers['Fresh'], c=customers['labels'], cmap='viridis', label='labels')\n",
+    "\n",
+    "axs[0].set_xlabel('Grocery')\n",
+    "axs[0].set_ylabel('Fresh')\n",
+    "\n",
+    "axs[1].scatter(customers['Grocery'], customers['Fresh'], c=customers['label_dbscan'], cmap='viridis', label='label_dbscan')\n",
+    "\n",
+    "axs[1].set_xlabel('Grocery')\n",
+    "axs[1].set_ylabel('Fresh')\n",
+    "\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -266,11 +1320,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here:"
+    "# Your code here:\n",
+    "fig, axs = plt.subplots(1, 2,  figsize=(12, 5))\n",
+    "\n",
+    "axs[0].scatter(customers['Frozen'], customers['Delicassen'], c=customers['labels'], cmap='viridis', label='labels')\n",
+    "\n",
+    "axs[0].set_xlabel('Frozen')\n",
+    "axs[0].set_ylabel('Delicassen')\n",
+    "\n",
+    "axs[1].scatter(customers['Frozen'], customers['Delicassen'], c=customers['label_dbscan'], cmap='viridis', label='label_dbscan')\n",
+    "\n",
+    "axs[1].set_xlabel('Frozen')\n",
+    "axs[1].set_ylabel('Delicassen')\n",
+    "\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -282,11 +1361,136 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicassen</th>\n",
+       "      <th>label_dbscan</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>labels</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.674603</td>\n",
+       "      <td>8130.031746</td>\n",
+       "      <td>8874.071429</td>\n",
+       "      <td>14139.150794</td>\n",
+       "      <td>1339.476190</td>\n",
+       "      <td>6104.936508</td>\n",
+       "      <td>1542.706349</td>\n",
+       "      <td>-0.523810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>13297.947368</td>\n",
+       "      <td>3168.306220</td>\n",
+       "      <td>3740.588517</td>\n",
+       "      <td>3436.971292</td>\n",
+       "      <td>769.392344</td>\n",
+       "      <td>1262.511962</td>\n",
+       "      <td>0.315789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.846154</td>\n",
+       "      <td>2.615385</td>\n",
+       "      <td>25770.769231</td>\n",
+       "      <td>35160.384615</td>\n",
+       "      <td>41977.384615</td>\n",
+       "      <td>6844.538462</td>\n",
+       "      <td>19867.384615</td>\n",
+       "      <td>7880.307692</td>\n",
+       "      <td>-1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.054348</td>\n",
+       "      <td>1.315217</td>\n",
+       "      <td>12407.130435</td>\n",
+       "      <td>3401.771739</td>\n",
+       "      <td>4234.130435</td>\n",
+       "      <td>4082.282609</td>\n",
+       "      <td>864.739130</td>\n",
+       "      <td>1198.402174</td>\n",
+       "      <td>1.326087</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Channel    Region         Fresh          Milk       Grocery  \\\n",
+       "labels                                                                 \n",
+       "0       2.000000  2.674603   8130.031746   8874.071429  14139.150794   \n",
+       "1       1.000000  3.000000  13297.947368   3168.306220   3740.588517   \n",
+       "2       1.846154  2.615385  25770.769231  35160.384615  41977.384615   \n",
+       "3       1.054348  1.315217  12407.130435   3401.771739   4234.130435   \n",
+       "\n",
+       "             Frozen  Detergents_Paper   Delicassen  label_dbscan  \n",
+       "labels                                                            \n",
+       "0       1339.476190       6104.936508  1542.706349     -0.523810  \n",
+       "1       3436.971292        769.392344  1262.511962      0.315789  \n",
+       "2       6844.538462      19867.384615  7880.307692     -1.000000  \n",
+       "3       4082.282609        864.739130  1198.402174      1.326087  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "mean_kmeans = customers.groupby('labels').mean()\n",
+    "mean_kmeans_df = pd.DataFrame(mean_kmeans)\n",
+    "\n",
+    "mean_dbscan = customers.groupby('label_dbscan').mean()\n",
+    "mean_dbscan_df = pd.DataFrame(mean_dbscan)\n",
+    "\n",
+    "mean_kmeans_df\n"
    ]
   },
   {
@@ -378,7 +1582,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,